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New stellar activity probe beats standard proxies by factor of two

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2026-07-08 09:18 UTC pith:VODX23PJ

load-bearing objection SRA velocity shift may partly track residual Doppler rather than pure activity, but the paper's own injection tests bound the problem honestly. the 2 major comments →

arxiv 2607.06339 v1 pith:VODX23PJ submitted 2026-07-07 astro-ph.SR astro-ph.EPastro-ph.IM

Spectral Ratio Analysis: probing of a new suite of stellar activity indicators as a tool for astrophysical noise mitigation

Jean C. Costes , Christopher A. Watson , Ernst de Mooij , Katlyn L. Hobbs , Dana Clarice S. Yaptangco , Yvonne C. Unruh , Megan Bedell , Nad\`ege Meunier
show 1 more author
Thomas D. Mitchell
This is my paper
classification astro-ph.SR astro-ph.EPastro-ph.IM
keywords stellaractivityspectralindicatorsphotosphericvariabilityanalysisastrophysical
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Spectral Ratio Analysis (SRA), a method that isolates stellar-activity signatures by dividing high-activity spectra by a low-activity template, directly in the photospheric absorption lines where radial velocities are measured. From hundreds of activity-sensitive spectral features identified across 14 G- and K-type stars, the authors extract two summary metrics: the SRA Feature amplitude (tracking the strength of line distortions) and the SRA Feature velocity shift (tracking the apparent motion of active regions across the stellar disk). The central claim is that these two metrics capture stellar-activity-induced radial-velocity variability better than the standard chromospheric index logR'HK and cross-correlation-function-based proxies (BIS and FWHM) by up to a factor of two, as demonstrated through linear regression, random forest importance ranking, and planetary injection-recovery tests. The velocity shift metric carries most of the predictive power, acting as a directional probe of where active regions sit on the stellar surface — information that scalar indicators like logR'HK cannot encode.

Core claim

The paper's core discovery is that activity-sensitive spectral features, extracted by dividing high-activity stellar spectra by a low-activity template, yield a velocity-shift metric that predicts stellar-activity-induced radial-velocity variations more effectively than the chromospheric activity index logR'HK or cross-correlation-function shape parameters (BIS, FWHM). In a case study on alpha Cen B, residual RMS drops from 2.3 m/s with logR'HK to 1.0 m/s with SRA; across the full 14-star sample, SRA-based fits yield lower residual RMS than any classical indicator combination for the majority of stars, and random forest regression ranks the SRA Feature velocity shift as the most important RV

What carries the argument

The SRA process: (1) correct spectra for barycentric motion, secular acceleration, and known planets; (2) divide each high-activity spectrum by a low-activity seasonal template to produce ratio spectra; (3) remove instrumental ripple artefacts via a wavelength-dependent sinusoidal model; (4) identify activity-sensitive features as clusters of pixels exceeding twice the noise level in the ratio spectrum; (5) fit Gaussians to each feature to extract amplitude and velocity shift; (6) combine all features into two global metrics per observation — the weighted-average SRA Feature amplitude and weighted-average SRA Feature velocity shift. These two metrics serve as the activity indicators tested.

Load-bearing premise

The SRA Feature velocity shift is assumed to primarily reflect activity-induced line-shape distortions rather than artefacts from residual Doppler misalignment between the active and template spectra. The authors test ±5 m/s misalignments and inspect for characteristic S-shaped residuals, but the separation between genuine activity signals and misalignment artefacts becomes degenerate when uncorrected planetary signals or barycentric errors persist at the m/s level. This is a

What would settle it

Inject a known planetary signal at 1-2 m/s amplitude into a well-sampled star, then attempt recovery after subtracting the SRA-based activity model. If the velocity shift metric is partly tracking residual Doppler shifts rather than genuine line-shape changes, the recovered planet parameters will be biased or the SRA residuals will show the injected signal partially absorbed — as the authors themselves observed at 5-10 m/s injection levels.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 8 minor

Summary. This paper presents a proof-of-concept demonstration of Spectral Ratio Analysis (SRA) applied to 14 G- and K-type stars observed with HARPS. The method divides high-activity spectra by a low-activity template to isolate activity-sensitive photospheric features, from which two global metrics are derived: the SRA Feature amplitude and the SRA Feature velocity shift. These metrics are compared against classical activity proxies (logR'HK, CCF BIS, CCF FWHM) in their ability to track stellar-activity-induced RV variations, using linear regression, random forest regression, and planetary injection-recovery tests. The authors find that SRA indicators — particularly the SRA Feature velocity shift — outperform classical proxies by up to a factor of two in RMS residual reduction for the majority of stars, and that the SRA Feature amplitude correlates with logR'HK while the velocity shift does not on nightly timescales but shows a moderate seasonal correlation for cooler stars. The paper also identifies and corrects instrumental ripple structures in the ratio spectra and provides a publicly available atlas of SRA spectra and features on Zenodo.

Significance. The paper tackles a central problem in exoplanet RV science: the mitigation of stellar activity noise. The SRA approach is a natural extension of Thompson et al. (2017, 2020) to a larger stellar sample, and the direct extraction of velocity information from photospheric features is a genuinely interesting contribution. The authors deserve credit for making their data publicly available (Zenodo atlas), for testing multiple independent methods (linear regression, random forest, injection recovery), and for honestly acknowledging key limitations including the SNR threshold, the simplified two-metric approach, and the vulnerability to Doppler misalignment. The planetary injection-recovery tests (Section 4.4) provide a concrete and falsifiable demonstration of the method's practical utility and its breakdown regime. The identification of a consistent thin-film optical path length (NL ~ 1.5e7 Å) across all targets is a useful instrumental by-product.

major comments (2)
  1. Section 4.1 and Section 4.4: The central claim — that SRA indicators outperform logR'HK — is most vulnerable to the concern that the SRA Feature velocity shift may partly absorb residual Doppler shifts rather than purely activity-induced line-shape distortions. The authors' mitigation test (injecting ±5 m/s misalignments, Section 4.1, Figure 5 red error bars) checks whether a uniform offset degrades the fit, and the injection-recovery tests (Section 4.4) show that at 10 m/s the planetary signal is absorbed by SRA. These tests are valuable and the authors are commended for including them. However, the regime of greatest concern is not a uniform offset but a time-varying residual velocity that is correlated with the CCF RVs by construction (since both are derived from the same spectra). The ±5 m/s test applies a constant shift; it does not directly probe what happens when a residual Doppl!
  2. Section 5, final paragraphs: The authors note that using a simple median to combine feature information yields better results than the weighted average used throughout the paper (lower RMS ratios; 11/14 stars dominated by SRA Feature shift importance instead of 8/14). This is a non-trivial internal inconsistency: the headline results (Figure 5, Table E1, Table F1) are all based on the weighted-average combination, but the authors themselves state that a crude median outperforms it. This weakens the specific quantitative claims (e.g., 'factor of two' improvement) since those numbers depend on a combination method the authors acknowledge is suboptimal. The authors should either adopt the median combination for the main results or explicitly quantify how much the improvement changes, so that the headline claims are tied to the best-performing method.
minor comments (8)
  1. Section 2.2: The statement that CCF RVs were not used for alignment because they include activity-induced shifts is important and correct, but the subsequent statement that known planets were removed using allesfitter with activity-proxy linear correlations could be stated more precisely — it is unclear whether the activity proxies were included as nuisance parameters in the orbital fit or used to detrend the RVs before fitting.
  2. Section 3.1: The interpretation that weaker amplitude-logR'HK correlations in earlier-type stars reflect a decoupling of photosphere and chromosphere is plausible but speculative given that the two hottest stars have the sparsest data. The authors acknowledge this, but the phrasing 'may hint at an underlying astrophysical effect' could be tempered further.
  3. Table E1: Two cells are described as 'highlighted in red' but no red highlighting is visible in the table as rendered. The authors should ensure the highlighting is preserved (e.g., via bold text or an asterisk).
  4. Section 4.3: The random forest regression uses 90% of data for training and 10% for testing, but the figure caption does not specify whether this is a single split or cross-validated. The jackknife error bars on the importances should be clarified as to whether they reflect split-to-split variation or feature subsampling.
  5. Figure 5, lower panel: The y-axis label and the ratio definition should be explicitly stated in the caption (i.e., RMS(RV_SRA) / RMS(RV_logR'HK)).
  6. Section 2.3, Eq. (1): The ripple model includes a wavelength-dependent frequency term (lambda_n / lambda), but the physical motivation for this specific functional form versus the thin-film model of Eq. (2) is not fully explained. Since both give similar results, it would help to state which was adopted for the final analysis and why.
  7. The Abstract states 'by up to a factor of two' — this is supported by Table E1 for individual stars (e.g., alpha Cen B: 3.1 to 1.4 m/s), but the sample-wide improvement is more modest for most stars. 'Up to a factor of two for the best-case targets' would be more precise.
  8. Section 4.1, Table 1: The coefficients are normalised between -1 and 1, but it would help to also report the raw coefficient values or at least the typical scale of the SRA Feature amplitude and shift metrics, so readers can assess the physical magnitude of the fitted relationship.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for a careful and constructive report. The two major comments are addressed below. On the first, we agree that the existing ±5 m/s constant-offset test does not fully probe the regime of time-varying residual Doppler shifts correlated with the CCF RVs, and we will add a dedicated test for this scenario. On the second, we agree that the internal inconsistency between the weighted-average and median combination methods needs to be resolved in the manuscript, and we will adopt the better-performing median as the primary method for the headline results.

read point-by-point responses

  1. Referee: Section 4.1 and Section 4.4: The central claim — that SRA indicators outperform logR'HK — is most vulnerable to the concern that the SRA Feature velocity shift may partly absorb residual Doppler shifts rather than purely activity-induced line-shape distortions. The ±5 m/s test applies a constant shift; it does not directly probe what happens when a time-varying residual velocity correlated with the CCF RVs is present.

    Authors: The referee is correct that our existing ±5 m/s test, which applies a constant offset, does not directly probe the most dangerous regime: a time-varying residual Doppler shift that is correlated with the CCF RVs by construction. We agree this is the most important vulnerability of the SRA Feature velocity shift, and we will address it explicitly in the revised manuscript. Specifically, we will add a new test in which we inject a time-varying sinusoidal velocity signal (at the same periods as the stellar rotation and at arbitrary periods) into the spectra before constructing the ratio spectra, with amplitudes comparable to the observed CCF RV scatter (2–5 m/s). This directly simulates the scenario the referee describes: a residual Doppler shift that is correlated with the measured CCF RVs. We will measure how much the SRA Feature velocity shift absorbs this injected signal and quantify the resulting artificial improvement in the RV_SRA fit. We expect partial absorption — the injection-recovery tests in Section 4.4 already show that at 10 m/s the planetary signal is substantially absorbed by SRA — but the key question is whether the absorption is sufficient to fully explain the factor-of-two improvement over logR'HK. Based on the fact that (i) the SRA features show distinct morphologies inconsistent with pure Doppler misalignment (no S-shaped residuals), (ii) different lines of similar depth and width show very different feature amplitudes, and (iii) the SRA Feature amplitude (which is insensitive to Doppler shifts) also contributes to the improvement, we expect the residual improvement after accounting for Doppler absorption to remain significant. However, we will present the quantitative results honestly and will revise the headline claims if the test shows that Doppler revision: partial

  2. Referee: Section 5, final paragraphs: The authors note that using a simple median to combine feature information yields better results than the weighted average used throughout the paper (lower RMS ratios; 11/14 stars dominated by SRA Feature shift importance instead of 8/14). This is a non-trivial internal inconsistency: the headline results are all based on the weighted-average combination, but the authors themselves state that a crude median outperforms it. The authors should either adopt the median combination for the main results or explicitly quantify how much the improvement changes.

    Authors: The referee is correct that this is a non-trivial internal inconsistency that weakens the specific quantitative claims. We will adopt the median combination as the primary method for the main results in the revised manuscript. This means that Figure 5 (lower panel), Table E1, and Table F1 will be regenerated using the median combination. We will also report the weighted-average results for comparison, either in an appendix or as a secondary panel, so that readers can see the difference. The headline claim of 'up to a factor of two' improvement will be tied to the best-performing method (median). Based on our preliminary results, the median combination yields lower RMS ratios for all 14 stars and increases the number of stars where the SRA Feature shift dominates the random forest importance from 8/14 to 11/14, so the headline claims are if anything strengthened. We will also expand the discussion in Section 5 to note that the weighted average appears to be degraded by issues in the Gaussian fitting (where stronger features with higher weights may have diminished velocity shifts due to physical differences in how stellar activity impacts different lines), and that the median is more robust to these effects. We agree with the referee that tying the headline claims to the best-performing method is essential. revision: yes

Circularity Check

0 steps flagged

No significant circularity: SRA indicators are derived from spectra independently of the CCF RVs they are regressed against; self-citations are methodological, not load-bearing for the central claim.

full rationale

The paper's central claim is that SRA-derived indicators (Feature amplitude and Feature velocity shift) better track CCF RV variability than classical proxies (logR'HK, BIS, FWHM). Walking the derivation chain: (1) SRA indicators are extracted from ratio spectra built by dividing high-activity by low-activity template spectra (Section 2.2). The CCF RVs being modelled are the DRS pipeline outputs from the same spectra, but the SRA metrics are not defined in terms of the CCF RVs — they are Gaussian-fit amplitudes and centroid shifts of activity-sensitive features in the ratio spectra. (2) The linear regression (Eq. 3) fits CCF RVs as a function of SRA indicators (or logR'HK), so the SRA indicators are inputs and the CCF RVs are the target — not the reverse. (3) The authors explicitly avoid using DRS CCF RVs for wavelength alignment (Section 2.2: 'we did not use the RVs derived from the DRS cross-correlation functions... as they include apparent shifts due to activity-induced line-shape changes'), instead correcting only for barycentric motion, secular acceleration, and known planets. This is a deliberate methodological choice to prevent circularity. (4) The ±5 m/s misalignment injection test (Section 4.1, Figure 5) and the planetary injection recovery tests (Section 4.4, Figure 7) provide independent checks that the SRA-RV correlation is not an artefact of shared Doppler information. The 10 m/s injection case does show SRA absorbing the planetary signal (producing S-shaped residuals), which the authors acknowledge as a limitation — but this is a correctness/limitation concern, not circularity. (5) Self-citations to Thompson et al. (2017, 2020) establish the SRA methodology and its solar validation, but the present paper's central comparative claim (SRA vs. logR'HK across 14 stars) does not reduce to those citations by construction. The random forest analysis (Section 4.3) uses the same indicators as inputs to predict CCF RVs, which is standard regression — not circular. The shared-spectra concern (SRA and CCF RVs from the same observations) is a legitimate methodological caveat that the authors address, but it does not constitute circularity in the sense of the prediction being equivalent to the input by definition. The derivation is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

9 free parameters · 4 axioms · 0 invented entities

The paper introduces no new physical entities, particles, forces, or dimensions. The SRA Feature amplitude and SRA Feature shift are new metrics, not new physical objects. The thin-film interference model for ripples (Section 2.3) is an instrumental diagnosis, not a new entity. Free parameters are mostly selection thresholds and empirical correction parameters, which are standard for observational astronomy. The key ad-hoc axiom is that the weighted-average global metrics preserve meaningful activity information, which the authors themselves question when the median combination performs better (Section 5).

free parameters (9)
  • SNR threshold (nightly) = 150
    Set based on features identified for the Sun in Thompson et al. (2020); not derived from the current data.
  • SNR threshold (seasonal) = 500
    Chosen to ensure adequate data quality for combined spectra; not derived.
  • logR'HK upper limit = -4.80
    Selection criterion for relatively inactive stars; adopted from Costes et al. (2021).
  • Delta_act lower limit = 0.05
    Minimum activity cycle variation required for SRA; chosen by the authors.
  • Gaussian filter sigma (ripple removal) = 15 pixels
    Adopted for flattening residual trends in ratio spectra; tested against polynomial fitting but the value itself is not optimized.
  • Feature FWHM cutoff = 2.5 km/s
    Comparable to HARPS instrumental resolution; used to discard spurious features.
  • Velocity shift cutoff = 4.5 km/s
    Conservative cutoff adopted because v sin i values are uncertain for slow rotators.
  • Ripple model NL (thin-film) = 1.5e7 Angstrom
    Fitted optical path length from observed ripple patterns across orders and stars.
  • Linear regression coefficients (alpha, beta) = varies per star/season
    Fitted coefficients in Equation 3 for RV modelling; not free parameters in the theoretical sense but fitted to data.
axioms (4)
  • domain assumption Stellar activity-induced RV variations are primarily driven by changes in photospheric absorption line profiles (facular suppression of convective blueshift, spot/facula contrast).
    Underlies the entire SRA approach; invoked in Section 1 and Section 4. Supported by Haywood et al. 2016, Meunier et al. 2024.
  • domain assumption Dividing a high-activity spectrum by a low-activity template isolates activity-driven spectral changes.
    Fundamental assumption of SRA; invoked in Section 2.2. The template choice (lowest-activity seasonal average) is assumed to represent a clean baseline.
  • domain assumption The identified activity-sensitive features are astrophysical in origin and not dominated by instrumental systematics after ripple correction.
    Required for all downstream analysis; invoked implicitly throughout Sections 3-4. The ripple correction (Section 2.3) is empirical and its completeness is not fully verified.
  • ad hoc to paper A weighted average of Gaussian-fit amplitudes and shifts across all features captures meaningful global activity information.
    The authors acknowledge this is a 'grossly simplified view' (Section 3). The choice of weighted average (vs. median, which performed better) is not theoretically motivated.

pith-pipeline@v1.1.0-glm · 31526 in / 3358 out tokens · 550013 ms · 2026-07-08T09:18:04.255855+00:00 · methodology

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read the original abstract

Stellar activity is the main barrier to detecting and/or confirming low-mass/long-period (and Earth-analogue) planets using radial-velocity (RV) measurements. Searching for reliable indicators that better trace magnetic activity may be key for both distinguishing more clearly between stellar and planetary signals, and for probing the underlying physics occurring on the stellar surface. In this work we have compared observations taken for magnetically active and inactive stellar phases over multiple time scales to study the spectral imprint due to varying stellar activity. This serves as a proof-of-concept demonstration of a technique (named Spectral Ratio Analysis, SRA) that can be used to isolate activity-driven changes directly in the stellar photospheric absorption lines where RVs are measured. Using 14 relatively quiet and well sampled G- and K-type stars that show stellar activity cycles, we identified hundreds of activity-sensitive spectral features. Reducing this variability information into two global metrics -- amplitude and velocity shift -- uncovers potential evidence of a decoupling of the photospheric and chromospheric responses to stellar activity in earlier-type stars. Additionally, potential signatures of the variations in the magnitude of the suppression of the convective blueshift throughout the activity cycle were observed via SRA. Finally, we show that these SRA indicators better capture RV variability than classical activity proxies, such as the chromospheric logR'HK index and other cross-correlation function-based parameters such as BIS and FWHM, by up to a factor of two. The direct link between photospheric line behaviour and stellar-induced RV variability offers a promising path for improving astrophysical noise mitigation.

Figures

Figures reproduced from arXiv: 2607.06339 by Christopher A. Watson, Dana Clarice S. Yaptangco, Ernst de Mooij, Jean C. Costes, Katlyn L. Hobbs, Megan Bedell, Nad\`ege Meunier, Thomas D. Mitchell, Yvonne C. Unruh.

Figure 1
Figure 1. Figure 1: A selection of seasonal ratio spectra for the 14 stars used in this study, ordered by 𝐵 − 𝑉 (as presented in Table A1) and removed from the ripple-like structure. The ratio spectra were generated by dividing high-activity spectra by a low-activity template for each star (SRA). For comparison, all the spectra are shown in their rest wavelength and, in order to increase the signal-to-noise, we have combined … view at source ↗
Figure 2
Figure 2. Figure 2: Observation and removal of the ‘ripple’-like structure in SRA. The top panel presents a portion of the nightly ratio spectra for the target 𝛼 Cen B. As can be seen, some patterns appear irregularly on some of the nightly ratio spectra. The bottom panel presents the same portion of the nightly ratio spectra after the correction of the ripples using Equation 1, enhancing the visibility of the activity-induce… view at source ↗
Figure 3
Figure 3. Figure 3: A graphical outline of the process of detecting activity-sensitive features in the ratio spectra of 𝛼 Cen B. The first panel (a) presents the low￾activity template used to create the nightly and seasonal ratio spectra. The second panel (b) shows the eight seasonal ratio spectra created, offset from one another for clarity and ordered by their median log 𝑅 ′ HK, with the most active at the top. The third pa… view at source ↗
Figure 4
Figure 4. Figure 4: Pearson’s 𝑅 correlation between the SRA parameters, SRAFeature amplitudes and shifts, and log 𝑅 ′ HK for each star. The upper panels present the correlation over the short time scale using the nightly data. Conversely, the lower panels present the correlation over the long time scale using the seasonal data. A different shape was assigned to represent the median value of the weighted average SNR in the 55t… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of the linear regression model of the CCF RVs using log 𝑅 ′ HK and the SRA indicators. The upper panels show the measured SRAFeature velocity shift values, in green, for the K1V star 𝛼 Cen B, where each panel focusses on a specific season. These panels serve as an example to present how the SRAFeature shifts is measuring the effect of active regions on the surface of a star. The second row of pa… view at source ↗
Figure 6
Figure 6. Figure 6: Probing the importance of log 𝑅 ′ HK, SRAFeature amplitudes and SRAFeature shifts using random forest regression. The upper panels show the CCF RVs and the results of the random forest regression for 𝛼 Cen B in black and red, respectively, and offset from one another for clarity. Similar to [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of different planetary signals injection and their recovery in the residuals. The left panels represent, in black, the CCF RV periodograms of 𝛼 Cen B after injecting five planetary signals (one signal per panel), with a semi-amplitude of 10, 5, 2, 1 and 0.5 m s−1 at an orbital period of 100 d. The fake planetary signal is marked with a vertical red dashed line and the 0.1% FAP level is shown wit… view at source ↗

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